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Hattab T, König SD, Carlson DC, Hayes RF, Sha Z, Park MC, Kahn L, Patel S, McGovern RA, Henry T, Khan F, Herman AB, Darrow DP. Assessing expert reliability in determining intracranial EEG channel quality and introducing the automated bad channel detection algorithm. J Neural Eng 2024; 21:046028. [PMID: 38981500 DOI: 10.1088/1741-2552/ad60f6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 07/09/2024] [Indexed: 07/11/2024]
Abstract
Objective.To evaluate the inter- and intra-rater reliability for the identification of bad channels among neurologists, EEG Technologists, and naïve research personnel, and to compare their performance with the automated bad channel detection (ABCD) algorithm for detecting bad channels.Approach.Six Neurologists, ten EEG Technologists, and six naïve research personnel (22 raters in total) were asked to rate 1440 real intracranial EEG channels as good or bad. Intra- and interrater kappa statistics were calculated for each group. We then compared each group to the ABCD algorithm which uses spectral and temporal domain features to classify channels as good or bad.Main results.Analysis of channel ratings from our participants revealed variable intra-rater reliability within each group, with no significant differences across groups. Inter-rater reliability was moderate among neurologists and EEG Technologists but minimal among naïve participants. Neurologists demonstrated a slightly higher consistency in ratings than EEG Technologists. Both groups occasionally misclassified flat channels as good, and participants generally focused on low-frequency content for their assessments. The ABCD algorithm, in contrast, relied more on high-frequency content. A logistic regression model showed a linear relationship between the algorithm's ratings and user responses for predominantly good channels, but less so for channels rated as bad. Sensitivity and specificity analyses further highlighted differences in rating patterns among the groups, with neurologists showing higher sensitivity and naïve personnel higher specificity.Significance.Our study reveals the bias in human assessments of intracranial electroencephalography (iEEG) data quality and the tendency of even experienced professionals to overlook certain bad channels, highlighting the need for standardized, unbiased methods. The ABCD algorithm, outperforming human raters, suggests the potential of automated solutions for more reliable iEEG interpretation and seizure characterization, offering a reliable approach free from human biases.
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Affiliation(s)
- Tariq Hattab
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - Seth D König
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States of America
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - Danielle C Carlson
- Department of Neurology, Yale University, New Haven, CT 06510, United States of America
| | - Rebecca F Hayes
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States of America
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - Zhiyi Sha
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - Michael C Park
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - Lora Kahn
- Department of Neurosurgery, Ochsner Medical Center, New Orleans, LA 70121, United States of America
| | - Sima Patel
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - Robert A McGovern
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - Thomas Henry
- Department of Neurology, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - Fawad Khan
- Department of Neurology, Ochsner Medical Center, New Orleans, LA 70121, United States of America
| | - Alexander B Herman
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States of America
| | - David P Darrow
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States of America
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, United States of America
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Jedynak M, Boyer A, Mercier M, Chanteloup-Forêt B, Bhattacharjee M, Kahane P, David O. SEEG electrode shaft affects amplitude and latency of potentials evoked with single pulse electrical stimulation. J Neurosci Methods 2024; 403:110035. [PMID: 38128785 DOI: 10.1016/j.jneumeth.2023.110035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 12/07/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Long and thin shaft electrodes are implanted intracerebrally for stereoelectroencephalography (SEEG) in patients with pharmacoresistant focal epilepsies. Two adjacent contacts of one of such electrodes can deliver a train of single pulse electrical stimulations (SPES), and evoked potentials (EPs) are recorded on other contacts. In this study we assess if stimulating and recording on the same shaft, as opposed to different shafts, has an impact on common EP features. NEW METHOD We leverage the large volume of SEEG data gathered in the F-TRACT database and analyze data from nearly one thousand SEEG implantations in order to verify whether stimulation and recording from the same shaft influence the EP pattern. RESULTS We found that when the stimulated and the recording contacts were located on the same shaft, the mean and median amplitudes of an EP are greater, and its mean and median latencies are smaller than when the contacts were located on different shafts. This effect is small (Cohen's d ∼ 0.1), but robust (p-value < 10-3) across the SEEG database. COMPARISON WITH EXISTING METHOD(S) Our study is the first one to address this question. Due to the choice of commonly used EP features, our method is congruent with other studies. CONCLUSIONS The magnitude of the reported effect does not obligate all standard analyses to correct for it, unless they aim at high precision. The source of the effect is not clear. Manufacturers of SEEG electrodes could examine it and potentially minimize the effect in their future products.
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Affiliation(s)
- Maciej Jedynak
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
| | - Anthony Boyer
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Manuel Mercier
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | | | - Manik Bhattacharjee
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France; Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Philippe Kahane
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; Neurology Department, CHU Grenoble Alpes, Grenoble, France
| | - Olivier David
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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Ayyoubi AH, Fazli Besheli B, Quach MM, Gavvala JR, Goldman AM, Swamy CP, Bartoli E, Curry DJ, Sheth SA, Francis DJ, Ince NF. Benchmarking signal quality and spatiotemporal distribution of interictal spikes in prolonged human iEEG recordings using CorTec wireless brain interchange. Sci Rep 2024; 14:2652. [PMID: 38332136 PMCID: PMC10853182 DOI: 10.1038/s41598-024-52487-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/19/2024] [Indexed: 02/10/2024] Open
Abstract
Neuromodulation through implantable pulse generators (IPGs) represents an important treatment approach for neurological disorders. While the field has observed the success of state-of-the-art interventions, such as deep brain stimulation (DBS) or responsive neurostimulation (RNS), implantable systems face various technical challenges, including the restriction of recording from a limited number of brain sites, power management, and limited external access to the assessed neural data in a continuous fashion. To the best of our knowledge, for the first time in this study, we investigated the feasibility of recording human intracranial EEG (iEEG) using a benchtop version of the Brain Interchange (BIC) unit of CorTec, which is a portable, wireless, and externally powered implant with sensing and stimulation capabilities. We developed a MATLAB/SIMULINK-based rapid prototyping environment and a graphical user interface (GUI) to acquire and visualize the iEEG captured from all 32 channels of the BIC unit. We recorded prolonged iEEG (~ 24 h) from three human subjects with externalized depth leads using the BIC and commercially available clinical amplifiers simultaneously in the epilepsy monitoring unit (EMU). The iEEG signal quality of both streams was compared, and the results demonstrated a comparable power spectral density (PSD) in all the systems in the low-frequency band (< 80 Hz). However, notable differences were primarily observed above 100 Hz, where the clinical amplifiers were associated with lower noise floor (BIC-17 dB vs. clinical amplifiers < - 25 dB). We employed an established spike detector to assess and compare the spike rates in each iEEG stream. We observed over 90% conformity between the spikes rates and their spatial distribution captured with BIC and clinical systems. Additionally, we quantified the packet loss characteristic in the iEEG signal during the wireless data transfer and conducted a series of simulations to compare the performance of different interpolation methods for recovering the missing packets in signals at different frequency bands. We noted that simple linear interpolation has the potential to recover the signal and reduce the noise floor with modest packet loss levels reaching up to 10%. Overall, our results indicate that while tethered clinical amplifiers exhibited noticeably better noise floor above 80 Hz, epileptic spikes can still be detected successfully in the iEEG recorded with the externally powered wireless BIC unit opening the road for future closed-loop neuromodulation applications with continuous access to brain activity.
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Affiliation(s)
- Amir Hossein Ayyoubi
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Behrang Fazli Besheli
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Michael M Quach
- Department of Neurology, Texas Children's Hospital, Houston, TX, USA
| | | | - Alica M Goldman
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | | | - Eleonora Bartoli
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Daniel J Curry
- Department of Neurosurgery, Texas Children's Hospital, Houston, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - David J Francis
- Department of Psychology, University of Houston, Houston, TX, USA
| | - Nuri F Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA.
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA.
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Meier A, Kuzdeba S, Jackson L, Daliri A, Tourville JA, Guenther FH, Greenlee JDW. Lateralization and Time-Course of Cortical Phonological Representations during Syllable Production. eNeuro 2023; 10:ENEURO.0474-22.2023. [PMID: 37739786 PMCID: PMC10561542 DOI: 10.1523/eneuro.0474-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 08/15/2023] [Accepted: 08/28/2023] [Indexed: 09/24/2023] Open
Abstract
Spoken language contains information at a broad range of timescales, from phonetic distinctions on the order of milliseconds to semantic contexts which shift over seconds to minutes. It is not well understood how the brain's speech production systems combine features at these timescales into a coherent vocal output. We investigated the spatial and temporal representations in cerebral cortex of three phonological units with different durations: consonants, vowels, and syllables. Electrocorticography (ECoG) recordings were obtained from five participants while speaking single syllables. We developed a novel clustering and Kalman filter-based trend analysis procedure to sort electrodes into temporal response profiles. A linear discriminant classifier was used to determine how strongly each electrode's response encoded phonological features. We found distinct time-courses of encoding phonological units depending on their duration: consonants were represented more during speech preparation, vowels were represented evenly throughout trials, and syllables during production. Locations of strongly speech-encoding electrodes (the top 30% of electrodes) likewise depended on phonological element duration, with consonant-encoding electrodes left-lateralized, vowel-encoding hemispherically balanced, and syllable-encoding right-lateralized. The lateralization of speech-encoding electrodes depended on onset time, with electrodes active before or after speech production favoring left hemisphere and those active during speech favoring the right. Single-electrode speech classification revealed cortical areas with preferential encoding of particular phonemic elements, including consonant encoding in the left precentral and postcentral gyri and syllable encoding in the right middle frontal gyrus. Our findings support neurolinguistic theories of left hemisphere specialization for processing short-timescale linguistic units and right hemisphere processing of longer-duration units.
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Affiliation(s)
- Andrew Meier
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Scott Kuzdeba
- Graduate Program for Neuroscience, Boston University, Boston, MA 02215
| | - Liam Jackson
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Ayoub Daliri
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
- College of Health Solutions, Arizona State University, Tempe, AZ 85004
| | - Jason A Tourville
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Frank H Guenther
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
- Department of Biomedical Engineering, Boston University, Boston, MA 02215
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02215
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02215
| | - Jeremy D W Greenlee
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA 52242
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Seguin C, Jedynak M, David O, Mansour S, Sporns O, Zalesky A. Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation. Neuron 2023; 111:1391-1401.e5. [PMID: 36889313 DOI: 10.1016/j.neuron.2023.01.027] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/28/2022] [Accepted: 01/30/2023] [Indexed: 03/09/2023]
Abstract
Communication between gray matter regions underpins all facets of brain function. We study inter-areal communication in the human brain using intracranial EEG recordings, acquired following 29,055 single-pulse direct electrical stimulations in a total of 550 individuals across 20 medical centers (average of 87 ± 37 electrode contacts per subject). We found that network communication models-computed on structural connectivity inferred from diffusion MRI-can explain the causal propagation of focal stimuli, measured at millisecond timescales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional, and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (R2=46% in data from held-out medical centers). Our work contributes toward the biological validation of concepts in network neuroscience and provides insight into how connectome topology shapes polysynaptic inter-areal signaling. We anticipate that our findings will have implications for research on neural communication and the design of brain stimulation paradigms.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Maciej Jedynak
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Olivier David
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Sina Mansour
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; Cognitive Science Program, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA; Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
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Choe M, Jin SH, Kim JS, Chung CK. Propofol anesthesia-induced spatiotemporal changes in cortical activity with loss of external and internal awareness: An electrocorticography study. Clin Neurophysiol 2023; 149:51-60. [PMID: 36898318 DOI: 10.1016/j.clinph.2023.01.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 01/12/2023] [Accepted: 01/29/2023] [Indexed: 02/17/2023]
Abstract
OBJECTIVE To understand the underlying mechanism of consciousness, investigating spatiotemporal changes in the cortical activity during the induction phase of unconsciousness is important. Loss of consciousness induced by general anesthesia is not necessarily accompanied by a uniform inhibition of all cortical activities. We hypothesized that cortical regions involved in internal awareness would be suppressed after disruption of cortical regions involved in external awareness. Thus, we investigated temporal changes in cortex during induction of unconsciousness. METHODS We recorded electrocorticography data of 16 epilepsy patients and investigated power spectral changes during induction phase from awake state to unconsciousness. Temporal changes were assessed at 1) the start point and 2) the interval of normalized time between start and end of power change (Δ tnormalized). RESULTS We found that the power increased at frequencies < 46 Hz, and decreased in range of 62-150 Hz, in global channels. In temporal changes of power change, superior parietal lobule and dorsolateral prefrontal cortex started to change early, but the changes were completed over a prolonged interval, whereas angular gyrus and associative visual cortex showed a delayed change and rapid completion. CONCLUSIONS Loss of consciousness induced by general anesthesia results first from disrupted communication between self and external world, followed by disrupted communication within self, with decreased activities of superior parietal lobule and dorsolateral prefrontal cortex, and later, attenuated activities of angular gyrus. SIGNIFICANCE Our findings provided neurophysiological evidence for the temporal changes in consciousness components induced by general anesthesia.
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Affiliation(s)
- Mikyung Choe
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - Seung-Hyun Jin
- Neuroscience Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - June Sic Kim
- The Research Institute of Basic Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
| | - Chun Kee Chung
- Department of Brain and Cognitive Sciences, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea; Neuroscience Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea; Department of Neurosurgery, Seoul National University Hospital, Seoul, Republic of Korea.
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Nagarajan A, Robinson N, Guan C. Relevance-based channel selection in motor imagery brain-computer interface. J Neural Eng 2023; 20. [PMID: 36548997 DOI: 10.1088/1741-2552/acae07] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 12/22/2022] [Indexed: 12/24/2022]
Abstract
Objective.Channel selection in the electroencephalogram (EEG)-based brain-computer interface (BCI) has been extensively studied for over two decades, with the goal being to select optimal subject-specific channels that can enhance the overall decoding efficacy of the BCI. With the emergence of deep learning (DL)-based BCI models, there arises a need for fresh perspectives and novel techniques to conduct channel selection. In this regard, subject-independent channel selection is relevant, since DL models trained using cross-subject data offer superior performance, and the impact of inherent inter-subject variability of EEG characteristics on subject-independent DL training is not yet fully understood.Approach.Here, we propose a novel methodology for implementing subject-independent channel selection in DL-based motor imagery (MI)-BCI, using layer-wise relevance propagation (LRP) and neural network pruning. Experiments were conducted using Deep ConvNet and 62-channel MI data from the Korea University EEG dataset.Main Results.Using our proposed methodology, we achieved a 61% reduction in the number of channels without any significant drop (p = 0.09) in subject-independent classification accuracy, due to the selection of highly relevant channels by LRP. LRP relevance-based channel selections provide significantly better accuracies compared to conventional weight-based selections while using less than 40% of the total number of channels, with differences in accuracies ranging from 5.96% to 1.72%. The performance of the adapted sparse-LRP model using only 16% of the total number of channels is similar to that of the adapted baseline model (p = 0.13). Furthermore, the accuracy of the adapted sparse-LRP model using only 35% of the total number of channels exceeded that of the adapted baseline model by 0.53% (p = 0.81). Analyses of channels chosen by LRP confirm the neurophysiological plausibility of selection, and emphasize the influence of motor, parietal, and occipital channels in MI-EEG classification.Significance.The proposed method addresses a traditional issue in EEG-BCI decoding, while being relevant and applicable to the latest developments in the field of BCI. We believe that our work brings forth an interesting and important application of model interpretability as a problem-solving technique.
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Affiliation(s)
- Aarthy Nagarajan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Neethu Robinson
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
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Fabietti M, Mahmud M, Lotfi A, Kaiser MS. ABOT: an open-source online benchmarking tool for machine learning-based artefact detection and removal methods from neuronal signals. Brain Inform 2022; 9:19. [PMID: 36048345 PMCID: PMC9437165 DOI: 10.1186/s40708-022-00167-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/22/2022] [Indexed: 11/10/2022] Open
Abstract
Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in decoding the recorded signals; hence, they must be removed to facilitate unbiased decision-making for a given investigation. Due to the complex and elusive manifestation of artefacts in neuronal signals, computational techniques serve as powerful tools for their detection and removal. Machine learning (ML) based methods have been successfully applied in this task. Due to ML’s popularity, many articles are published every year, making it challenging to find, compare and select the most appropriate method for a given experiment. To this end, this paper presents ABOT (Artefact removal Benchmarking Online Tool) as an online benchmarking tool which allows users to compare existing ML-driven artefact detection and removal methods from the literature. The characteristics and related information about the existing methods have been compiled as a knowledgebase (KB) and presented through a user-friendly interface with interactive plots and tables for users to search it using several criteria. Key characteristics extracted from over 120 articles from the literature have been used in the KB to help compare the specific ML models. To comply with the FAIR (Findable, Accessible, Interoperable and Reusable) principle, the source code and documentation of the toolbox have been made available via an open-access repository.
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Affiliation(s)
- Marcos Fabietti
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - Mufti Mahmud
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK. .,Medical Technologies Innovation Facility, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK. .,Computing and Informatics Research Centre, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.
| | - Ahmad Lotfi
- Department of Computer Science, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK
| | - M Shamim Kaiser
- Institute of Information Technology, Jahangirnagar University, Dhaka, 1342, Savar, Bangladesh
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Besheli BF, Sha Z, Gavvala JR, Gurses C, Karamursel S, Quach MM, Curry DJ, Sheth SA, Francis DJ, Henry TR, Ince NF. A sparse representation strategy to eliminate pseudo-HFO events from intracranial EEG for seizure onset zone localization. J Neural Eng 2022; 19:10.1088/1741-2552/ac8766. [PMID: 35931045 PMCID: PMC9901915 DOI: 10.1088/1741-2552/ac8766] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 08/05/2022] [Indexed: 02/08/2023]
Abstract
Objective.High-frequency oscillations (HFOs) are considered a biomarker of the epileptogenic zone in intracranial EEG recordings. However, automated HFO detectors confound true oscillations with spurious events caused by the presence of artifacts.Approach.We hypothesized that, unlike pseudo-HFOs with sharp transients or arbitrary shapes, real HFOs have a signal characteristic that can be represented using a small number of oscillatory bases. Based on this hypothesis using a sparse representation framework, this study introduces a new classification approach to distinguish true HFOs from the pseudo-events that mislead seizure onset zone (SOZ) localization. Moreover, we further classified the HFOs into ripples and fast ripples by introducing an adaptive reconstruction scheme using sparse representation. By visualizing the raw waveforms and time-frequency representation of events recorded from 16 patients, three experts labeled 6400 candidate events that passed an initial amplitude-threshold-based HFO detector. We formed a redundant analytical multiscale dictionary built from smooth oscillatory Gabor atoms and represented each event with orthogonal matching pursuit by using a small number of dictionary elements. We used the approximation error and residual signal at each iteration to extract features that can distinguish the HFOs from any type of artifact regardless of their corresponding source. We validated our model on sixteen subjects with thirty minutes of continuous interictal intracranial EEG recording from each.Main results.We showed that the accuracy of SOZ detection after applying our method was significantly improved. In particular, we achieved a 96.65% classification accuracy in labeled events and a 17.57% improvement in SOZ detection on continuous data. Our sparse representation framework can also distinguish between ripples and fast ripples.Significance.We show that by using a sparse representation approach we can remove the pseudo-HFOs from the pool of events and improve the reliability of detected HFOs in large data sets and minimize manual artifact elimination.
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Affiliation(s)
| | - Zhiyi Sha
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
| | - Jay R. Gavvala
- Department of Neurology-Neurophysiology, Baylor College of Medicine, Houston, TX, USA
| | - Candan Gurses
- Department of Neurology, School of Medicine, Koç Üniversitesi, Istanbul, Turkey
| | - Sacit Karamursel
- Department of Physiology, School of Medicine, Koç Üniversitesi, Istanbul, Turkey
| | - Michael M. Quach
- Department of Neurology, Texas Children’s Hospital, Houston, Texas, USA
| | - Daniel J. Curry
- Department of Neurosurgery, Texas Children’s Hospital, Houston, Texas, USA
| | - Sameer A. Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - David J. Francis
- Department of Psychology, University of Houston, Houston, TX, USA
| | - Thomas R. Henry
- Department of Neurology, University of Minnesota, Minneapolis, MN, USA
| | - Nuri F. Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
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Jaroszynski C, Amorim-Leite R, Deman P, Perrone-Bertolotti M, Chabert F, Job-Chapron AS, Minotti L, Hoffmann D, David O, Kahane P. Brain mapping of auditory hallucinations and illusions induced by direct intracortical electrical stimulation. Brain Stimul 2022; 15:1077-1087. [PMID: 35952963 DOI: 10.1016/j.brs.2022.08.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 07/28/2022] [Accepted: 08/03/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND The exact architecture of the human auditory cortex remains a subject of debate, with discrepancies between functional and microstructural studies. In a hierarchical framework for sensory perception, simple sound perception is expected to take place in the primary auditory cortex, while the processing of complex, or more integrated perceptions is proposed to rely on associative and higher-order cortices. OBJECTIVES We hypothesize that auditory symptoms induced by direct electrical stimulation (DES) offer a window into the architecture of the brain networks involved in auditory hallucinations and illusions. The intracranial recordings of these evoked perceptions of varying levels of integration provide the evidence to discuss the theoretical model. METHODS We analyzed SEEG recordings from 50 epileptic patients presenting auditory symptoms induced by DES. First, using the Juelich cytoarchitectonic parcellation, we quantified which regions induced auditory symptoms when stimulated (ROI approach). Then, for each evoked auditory symptom type (illusion or hallucination), we mapped the cortical networks showing concurrent high-frequency activity modulation (HFA approach). RESULTS Although on average, illusions were found more laterally and hallucinations more posteromedially in the temporal lobe, both perceptions were elicited in all levels of the sensory hierarchy, with mixed responses found in the overlap. The spatial range was larger for illusions, both in the ROI and HFA approaches. The limbic system was specific to the hallucinations network, and the inferior parietal lobule was specific to the illusions network. DISCUSSION Our results confirm a network-based organization underlying conscious sound perception, for both simple and complex components. While symptom localization is interesting from an epilepsy semiology perspective, the hallucination-specific modulation of the limbic system is particularly relevant to tinnitus and schizophrenia.
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Affiliation(s)
- Chloé Jaroszynski
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France.
| | - Ricardo Amorim-Leite
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France
| | - Pierre Deman
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France
| | - Marcela Perrone-Bertolotti
- Univ. Grenoble Alpes, CNRS, UMR5105, Laboratoire Psychologie et NeuroCognition, LPNC, 38000, Grenoble, France
| | - Florian Chabert
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France
| | - Anne-Sophie Job-Chapron
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France
| | - Lorella Minotti
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France
| | - Dominique Hoffmann
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France
| | - Olivier David
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France; Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
| | - Philippe Kahane
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France.
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11
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Cecchi R, Vinckier F, Hammer J, Marusic P, Nica A, Rheims S, Trebuchon A, Barbeau EJ, Denuelle M, Maillard L, Minotti L, Kahane P, Pessiglione M, Bastin J. Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice. eLife 2022; 11:72440. [PMID: 35822700 PMCID: PMC9348847 DOI: 10.7554/elife.72440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 07/12/2022] [Indexed: 11/13/2022] Open
Abstract
Identifying factors whose fluctuations are associated with choice inconsistency is a major issue for rational decision theory. Here, we investigated the neuro-computational mechanisms through which mood fluctuations may bias human choice behavior. Intracerebral EEG data were collected in a large group of subjects (n=30) while they were performing interleaved quiz and choice tasks that were designed to examine how a series of unrelated feedbacks affect decisions between safe and risky options. Neural baseline activity preceding choice onset was confronted first to mood level, estimated by a computational model integrating the feedbacks received in the quiz task, and then to the weighting of option attributes, in a computational model predicting risk attitude in the choice task. Results showed that (1) elevated broadband gamma activity (BGA) in the ventromedial prefrontal cortex (vmPFC) and dorsal anterior insula (daIns) was respectively signaling periods of high and low mood, (2) increased vmPFC and daIns BGA respectively promoted and tempered risk taking by overweighting gain vs. loss prospects. Thus, incidental feedbacks induce brain states that correspond to different moods and bias the evaluation of risky options. More generally, these findings might explain why people experiencing positive (or negative) outcome in some part of their life tend to expect success (or failure) in any other.
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Affiliation(s)
| | | | - Jiri Hammer
- University Hospital in Motol, Prague, Czech Republic
| | - Petr Marusic
- University Hospital in Motol, Prague, Czech Republic
| | - Anca Nica
- Centre Hospitalier Universitaire de Rennes, Rennes, France
| | | | | | - Emmanuel J Barbeau
- Brain and Cognition Research Centre (CerCo), CNRS, University of Toulouse Paul Sabatier, Toulouse, France
| | - Marie Denuelle
- Centre Hospitalier Universitaire de Toulouse, Toulouse, France
| | | | - Lorella Minotti
- Centre Hospitalier Universitaire de Grenoble, Grenoble, France
| | - Philippe Kahane
- Centre Hospitalier Universitaire de Grenoble, Grenoble, France
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12
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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13
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Mirchi N, Warsi NM, Zhang F, Wong SM, Suresh H, Mithani K, Erdman L, Ibrahim GM. Decoding Intracranial EEG With Machine Learning: A Systematic Review. Front Hum Neurosci 2022; 16:913777. [PMID: 35832872 PMCID: PMC9271576 DOI: 10.3389/fnhum.2022.913777] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications.
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Affiliation(s)
- Nykan Mirchi
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nebras M. Warsi
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Frederick Zhang
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Simeon M. Wong
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Hrishikesh Suresh
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Karim Mithani
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Lauren Erdman
- Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Hospital for Sick Children, Toronto, ON, Canada
| | - George M. Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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14
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GATSMOTE: Improving Imbalanced Node Classification on Graphs via Attention and Homophily. MATHEMATICS 2022. [DOI: 10.3390/math10111799] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
In recent decades, non-invasive neuroimaging techniques and graph theories have enabled a better understanding of the structural patterns of the human brain at a macroscopic level. As one of the most widely used non-invasive techniques, an electroencephalogram (EEG) may collect non-neuronal signals from “bad channels”. Automatically detecting these bad channels represents an imbalanced classification task; research on the topic is rather limited. Because the human brain can be naturally modeled as a complex graph network based on its structural and functional characteristics, we seek to extend previous imbalanced node classification techniques to the bad-channel detection task. We specifically propose a novel edge generator considering the prominent small-world organization of the human brain network. We leverage the attention mechanism to adaptively calculate the weighted edge connections between each node and its neighboring nodes. Moreover, we follow the homophily assumption in graph theory to add edges between similar nodes. Adding new edges between nodes sharing identical labels shortens the path length, thus facilitating low-cost information messaging.
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15
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Berezutskaya J, Vansteensel MJ, Aarnoutse EJ, Freudenburg ZV, Piantoni G, Branco MP, Ramsey NF. Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film. Sci Data 2022; 9:91. [PMID: 35314718 PMCID: PMC8938409 DOI: 10.1038/s41597-022-01173-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/24/2022] [Indexed: 12/19/2022] Open
Abstract
Intracranial human recordings are a valuable and rare resource of information about the brain. Making such data publicly available not only helps tackle reproducibility issues in science, it helps make more use of these valuable data. This is especially true for data collected using naturalistic tasks. Here, we describe a dataset collected from a large group of human subjects while they watched a short audiovisual film. The dataset has several unique features. First, it includes a large amount of intracranial electroencephalography (iEEG) data (51 participants, age range of 5-55 years, who all performed the same task). Second, it includes functional magnetic resonance imaging (fMRI) recordings (30 participants, age range of 7-47) during the same task. Eighteen participants performed both iEEG and fMRI versions of the task, non-simultaneously. Third, the data were acquired using a rich audiovisual stimulus, for which we provide detailed speech and video annotations. This dataset can be used to study neural mechanisms of multimodal perception and language comprehension, and similarity of neural signals across brain recording modalities.
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Affiliation(s)
- Julia Berezutskaya
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - Mariska J Vansteensel
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Erik J Aarnoutse
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Zachary V Freudenburg
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Giovanni Piantoni
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mariana P Branco
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nick F Ramsey
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
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16
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Lemaréchal JD, Jedynak M, Trebaul L, Boyer A, Tadel F, Bhattacharjee M, Deman P, Tuyisenge V, Ayoubian L, Hugues E, Chanteloup-Forêt B, Saubat C, Zouglech R, Reyes Mejia GC, Tourbier S, Hagmann P, Adam C, Barba C, Bartolomei F, Blauwblomme T, Curot J, Dubeau F, Francione S, Garcés M, Hirsch E, Landré E, Liu S, Maillard L, Metsähonkala EL, Mindruta I, Nica A, Pail M, Petrescu AM, Rheims S, Rocamora R, Schulze-Bonhage A, Szurhaj W, Taussig D, Valentin A, Wang H, Kahane P, George N, David O. A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials. Brain 2021; 145:1653-1667. [PMID: 35416942 PMCID: PMC9166555 DOI: 10.1093/brain/awab362] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 08/03/2021] [Accepted: 08/14/2021] [Indexed: 11/16/2022] Open
Abstract
Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics. To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.
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Affiliation(s)
- Jean-Didier Lemaréchal
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, Centre MEG-EEG and Experimental Neurosurgery Team, F-75013 Paris, France.,Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.,Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Maciej Jedynak
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Lena Trebaul
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Anthony Boyer
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - François Tadel
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Manik Bhattacharjee
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Pierre Deman
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Viateur Tuyisenge
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Leila Ayoubian
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Etienne Hugues
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | | | - Carole Saubat
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | - Raouf Zouglech
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France
| | | | - Sébastien Tourbier
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Patric Hagmann
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Claude Adam
- Department of Neurology, Epilepsy Unit, AP-HP, Hôpital de la Pitié Salpêtrière, F-75013 Paris, France
| | - Carmen Barba
- Neuroscience Department, Children's Hospital Meyer-University of Florence, Florence, Italy
| | - Fabrice Bartolomei
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.,Service de Neurophysiologie Clinique, APHM, Hôpitaux de la Timone, Marseille, France
| | - Thomas Blauwblomme
- Department of Pediatric Neurosurgery, Hôpital Necker-Enfants Malades, Université Paris V Descartes, Sorbonne Paris Cité, Paris, France
| | - Jonathan Curot
- Department of Neurophysiological Explorations, CerCo, CNRS, UMR5549, Centre Hospitalier Universitaire de Toulouse and University of Toulouse, Toulouse, France
| | - François Dubeau
- Montreal Neurological Institute and Hospital, Montreal, Canada
| | - Stefano Francione
- 'Claudio Munari' Centre for Epilepsy Surgery; Neuroscience Department, GOM, Niguarda, Milano, Italy
| | - Mercedes Garcés
- Multidisciplinary Epilepsy Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Edouard Hirsch
- University Hospital, Department of Neurology, Strasbourg, France
| | | | - Sinclair Liu
- Canton Sanjiu Brain Hospital Epilepsy Center, Jinan University, Guangzhou, China
| | - Louis Maillard
- Centre Hospitalier Universitaire de Nancy, Nancy, France
| | | | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania
| | - Anca Nica
- Neurology Department, CIC 1414, Rennes University Hospital; LTSI, INSERM U 1099, F-35000 Rennes, France
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic
| | | | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and Lyon's Neurosciences Research Center, INSERM U1028/CNRS UMR5292/Lyon 1 University, Lyon, France
| | - Rodrigo Rocamora
- Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - William Szurhaj
- Epilepsy Unit, Department of Clinical Neurophysiology, Lille University Medical Center, Lille, France
| | - Delphine Taussig
- Neurophysiology and Epilepsy Unit, Bicêtre Hospital, France.,Service de Neurochirurgie Pédiatrique, Fondation Rothschild, Paris, France
| | - Antonio Valentin
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, UK
| | - Haixiang Wang
- Yuquan Hospital Epilepsy Center, Tsinghua University, Beijing, China
| | - Philippe Kahane
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.,Neurology Department, CHU Grenoble Alpes, Grenoble, France
| | - Nathalie George
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, Inserm, CNRS, Centre MEG-EEG and Experimental Neurosurgery Team, F-75013 Paris, France
| | - Olivier David
- Université Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.,Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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Nagarajan A, Robinson N, Guan C. Investigation on Robustness of EEG-based Brain-Computer Interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6334-6340. [PMID: 34892562 DOI: 10.1109/embc46164.2021.9630031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Electroencephalogram (EEG)-based brain-computer interface (BCI) systems tend to suffer from performance degradation due to the presence of noise and artifacts in EEG data. This study is aimed at systematically investigating the robustness of state-of-the-art machine learning and deep learning based EEG-BCI models for motor imagery classification against simulated channel-specific noise in EEG data, at various low values of signal-to-noise ratio (SNR). Our results illustrate higher robustness of deep learning based MI classification models compared to the traditional machine learning based model, while identifying a set of channels with large sensitivity to simulated channel-specific noise. The EEGNet is relatively more robust towards channel-specific noise than Shallow ConvNet and FBCSP. We propose a preliminary solution, based on activation function, to improve the robustness of the deep learning models. By using saturating nonlinearities, the percentage drop in classification accuracy for SNR of -18 dB had reduced from 10.99% to 6.53% for EEGNet and 14.05% to 3.57% for Shallow ConvNet. Through this study, we emphasize the need for a more precise solution for enhancing the robustness, and thereby usability of EEG-BCI systems.
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Anatomical dissociation of intracerebral signals for reward and punishment prediction errors in humans. Nat Commun 2021; 12:3344. [PMID: 34099678 PMCID: PMC8184756 DOI: 10.1038/s41467-021-23704-w] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 05/06/2021] [Indexed: 11/17/2022] Open
Abstract
Whether maximizing rewards and minimizing punishments rely on distinct brain systems remains debated, given inconsistent results coming from human neuroimaging and animal electrophysiology studies. Bridging the gap across techniques, we recorded intracerebral activity from twenty participants while they performed an instrumental learning task. We found that both reward and punishment prediction errors (PE), estimated from computational modeling of choice behavior, correlate positively with broadband gamma activity (BGA) in several brain regions. In all cases, BGA scaled positively with the outcome (reward or punishment versus nothing) and negatively with the expectation (predictability of reward or punishment). However, reward PE were better signaled in some regions (such as the ventromedial prefrontal and lateral orbitofrontal cortex), and punishment PE in other regions (such as the anterior insula and dorsolateral prefrontal cortex). These regions might therefore belong to brain systems that differentially contribute to the repetition of rewarded choices and the avoidance of punished choices. Whether maximizing rewards and minimizing punishments rely on distinct brain learning systems remains debated. Here, using intracerebral recordings in humans, the authors provide evidence for brain regions differentially engaged in signaling reward and punishment prediction errors that prescribe repetition versus avoidance of past choices.
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Hosseini MP, Hosseini A, Ahi K. A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE Rev Biomed Eng 2021; 14:204-218. [PMID: 32011262 DOI: 10.1109/rbme.2020.2969915] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many different types of classifiers available to use, the analysis methods are also equally numerous. In this review, we will be examining specifically machine learning methods that have been developed for EEG analysis with bioengineering applications. We reviewed literature from 1988 to 2018 to capture previous and current classification methods for EEG in multiple applications. From this information, we are able to determine the overall effectiveness of each machine learning method as well as the key characteristics. We have found that all the primary methods used in machine learning have been applied in some form in EEG classification. This ranges from Naive-Bayes to Decision Tree/Random Forest, to Support Vector Machine (SVM). Supervised learning methods are on average of higher accuracy than their unsupervised counterparts. This includes SVM and KNN. While each of the methods individually is limited in their accuracy in their respective applications, there is hope that the combination of methods when implemented properly has a higher overall classification accuracy. This paper provides a comprehensive overview of Machine Learning applications used in EEG analysis. It also gives an overview of each of the methods and general applications that each is best suited to.
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Nair P, Aghoram R, Khilari M. Applications of artificial intelligence in epilepsy. INTERNATIONAL JOURNAL OF ADVANCED MEDICAL AND HEALTH RESEARCH 2021. [DOI: 10.4103/ijamr.ijamr_94_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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Li M, Liang Y, Yang L, Wang H, Yang Z, Zhao K, Shang Z, Wan H. Automatic bad channel detection in implantable brain-computer interfaces using multimodal features based on local field potentials and spike signals. Comput Biol Med 2020; 116:103572. [DOI: 10.1016/j.compbiomed.2019.103572] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 11/12/2019] [Accepted: 12/01/2019] [Indexed: 11/29/2022]
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Trebaul L, Deman P, Tuyisenge V, Jedynak M, Hugues E, Rudrauf D, Bhattacharjee M, Tadel F, Chanteloup-Foret B, Saubat C, Reyes Mejia GC, Adam C, Nica A, Pail M, Dubeau F, Rheims S, Trébuchon A, Wang H, Liu S, Blauwblomme T, Garcés M, De Palma L, Valentin A, Metsähonkala EL, Petrescu AM, Landré E, Szurhaj W, Hirsch E, Valton L, Rocamora R, Schulze-Bonhage A, Mindruta I, Francione S, Maillard L, Taussig D, Kahane P, David O. Probabilistic functional tractography of the human cortex revisited. Neuroimage 2018; 181:414-429. [PMID: 30025851 PMCID: PMC6150949 DOI: 10.1016/j.neuroimage.2018.07.039] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 06/21/2018] [Accepted: 07/15/2018] [Indexed: 12/13/2022] Open
Abstract
In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.
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Affiliation(s)
- Lena Trebaul
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Pierre Deman
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Viateur Tuyisenge
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Maciej Jedynak
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Etienne Hugues
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - David Rudrauf
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Manik Bhattacharjee
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - François Tadel
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Blandine Chanteloup-Foret
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Carole Saubat
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Gina Catalina Reyes Mejia
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Claude Adam
- Epilepsy Unit, Dept of Neurology, Pitié-Salpêtrière Hospital, APHP, Paris, France
| | - Anca Nica
- Neurology Department, CHU, Rennes, France
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic
| | - François Dubeau
- Montreal Neurological Institute and Hospital, Montreal, Canada
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of Lyon, Lyon, France
| | - Agnès Trébuchon
- Service de Neurophysiologie Clinique, APHM, Hôpitaux de la Timone, Marseille, France
| | - Haixiang Wang
- Yuquan Hospital Epilepsy Center, Tsinghua University, Beijing, China
| | - Sinclair Liu
- Canton Sanjiu Brain Hospital Epilepsy Center, Jinan University, Guangzhou, China
| | - Thomas Blauwblomme
- Department of Pediatric Neurosurgery, Hôpital Necker-Enfants Malades, Université Paris V Descartes, Sorbonne Paris Cité, Paris, France
| | - Mercedes Garcés
- Multidisciplinary Epilepsy Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Luca De Palma
- Department of Neuroscience, Bambino Gesù Children's Hospital, IRRCS, Rome, Italy
| | - Antonio Valentin
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, UK
| | | | | | | | - William Szurhaj
- Epilepsy Unit, Department of Clinical Neurophysiology, Lille University Medical Center, Lille, France
| | - Edouard Hirsch
- University Hospital, Department of Neurology, Strasbourg, France
| | - Luc Valton
- University Hospital, Department of Neurology, Toulouse, France
| | - Rodrigo Rocamora
- Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania
| | | | - Louis Maillard
- Centre Hospitalier Universitaire de Nancy, Nancy, France
| | - Delphine Taussig
- Service de neurochirurgie pédiatrique, Fondation Rothschild, Paris, France
| | - Philippe Kahane
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France; CHU Grenoble Alpes, Neurology Department, Grenoble, France
| | - Olivier David
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France.
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